Fairness-Aware Game Theoretic Approach for Service Management in Vehicular Clouds
Bibliographic record
Abstract
Vehicular cloud computing can perform a broad set of on-demand applications and services, which makes it highly suitable for urban settings. Despite a wide range of benefits to various services and applications by vehicular clouds, there are several issues and challenges that need to be carefully addressed in the context of provisioning services. This paper proposes a cooperative distributed game model to handle service management in vehicular clouds. Under this model, service providers play a cooperative game to maximize their total utility taking into consideration their recourse availability, current load, and total payoff. The proposed game has been implemented and evaluated using simulations with scenarios of light and heavy weight services. The game demonstrates that a cooperative technique leads players to handle higher number of services when compared to a non-cooperative setting. Furthermore, we also show that the proposed game mimics the behaviour of an optimization-based baseline solution. Through various simulation scenarios, we show that the proposed scheme introduces more than 85% similarity to the optimal solution when a few number of players participate, and its similarity to the optimal solution is improved to 99% when the number of the players increases by only 50%.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".